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math 135: Mathematics for Business and Economic Analysis I – Business Algebra (3)

Course Prerequisites/Corequisites: MATH 131 or a passing score on the mathematics portion of the TSI Assessment.

Course Description:Topics from college algebra (linear equations, quadratic equations, functions and graphs, inequalities), mathematics of finance (simple and compound interest, annuities), linear programming, matrices, systems of linear equations, applications to management, economics, and business. Listed as MATH 1324 in the Texas Common Course Numbering System.

Required Reading / Textbook: : College Mathematics for Business, Economics, Life Sciences and Social Sciences 12/e, by Barnett/Ziegler/Byleen

Major Assignments/Exams

  • Homework
  • Quizzes
  • Exams
  • Final Exam

Course topics

  1. A Beginning Library of Elementary Functions
  2. Linear Equations and Inequalities
  3. Graphs and Lines
  4. Linear Regression
  5. Additional Elementary Functions
  6. Functions
  7. Elementary Functions: Graphs and Transformations
  8. Quadratic Functions
  9. Polynomial and Rational Functions
  10. Exponential Functions
  11. Logarithmic Functions
  12. Mathematics of Finance
  13. Simple Interest
  14. Compound and Continuous Compound Interest
  15. Future Value of an Annuity; Sinking Funds
  16. Present Value of an Annuity; Amortization
  17. Systems of Linear Equations; Matrices
  18. Review: Systems of Linear Equations in Two Variables
  19. Systems of Linear Equations and Augmented Matrices
  20. Gauss-Jordan Elimination
  21. Matrices: Basic Operations
  22. Inverse of a Square Matrix
  23. Matrix Equations and Systems of Linear Equations
  24. Leontief Input-Output Analysis
  25. Linear Inequalities and Linear Programming
  26. Inequalities in Two Variables
  27. Systems of Linear Inequalities in Two Variables
  28. Linear Programming in Two Dimensions: A Geometric Approach
  29. Linear Programming: Simplex Method
  30. A Geometric Introduction to the Simplex Method
  31. The Simplex Method: Maximization with Problem Constraints of the Form
  32. The Dual; Minimization with Problem Constraints of the form
  33. Maximization and Minimization with Mixed Problem Constraints
  34. Logic, Sets, and Counting
  35. Logic
  36. Sets
  37. Basic Counting Principles
  38. Permutations and Combinations
  39. Probability
  40. Sample Spaces, Events, and Probability
  41. Union, Intersection, and Complement of Events; Odds
  42. Conditional Probability, Intersection, and Independence
  43. Bayes’ Formula
  44. Random Variables, Probability Distribution, and Expected Value
  45. Markov Chains
  46. Properties of Markov Chains
  47. Regular Markov Chains
  48. Absorbing Markov Chains

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